php-ml/tests/NeuralNetwork/Network/LayeredNetworkTest.php
Marcin Michalski db82afa263 Update to phpunit 8 and bump min php to 7.2 (#367)
* Update to phpunit 8

* Require at least PHP 7.2
2019-04-10 20:42:59 +02:00

74 lines
2.1 KiB
PHP

<?php
declare(strict_types=1);
namespace Phpml\Tests\NeuralNetwork\Network;
use Phpml\NeuralNetwork\ActivationFunction;
use Phpml\NeuralNetwork\Layer;
use Phpml\NeuralNetwork\Network\LayeredNetwork;
use Phpml\NeuralNetwork\Node\Input;
use PHPUnit\Framework\MockObject\MockObject;
use PHPUnit\Framework\TestCase;
class LayeredNetworkTest extends TestCase
{
public function testLayersSettersAndGetters(): void
{
$network = $this->getLayeredNetworkMock();
$network->addLayer($layer1 = new Layer());
$network->addLayer($layer2 = new Layer());
self::assertEquals([$layer1, $layer2], $network->getLayers());
}
public function testGetLastLayerAsOutputLayer(): void
{
$network = $this->getLayeredNetworkMock();
$network->addLayer($layer1 = new Layer());
self::assertEquals($layer1, $network->getOutputLayer());
$network->addLayer($layer2 = new Layer());
self::assertEquals($layer2, $network->getOutputLayer());
}
public function testSetInputAndGetOutput(): void
{
$network = $this->getLayeredNetworkMock();
$network->addLayer(new Layer(2, Input::class));
$network->setInput($input = [34, 43]);
self::assertEquals($input, $network->getOutput());
$network->addLayer(new Layer(1));
self::assertEquals([0.5], $network->getOutput());
}
public function testSetInputAndGetOutputWithCustomActivationFunctions(): void
{
$network = $this->getLayeredNetworkMock();
$network->addLayer(new Layer(2, Input::class, $this->getActivationFunctionMock()));
$network->setInput($input = [34, 43]);
self::assertEquals($input, $network->getOutput());
}
/**
* @return LayeredNetwork|MockObject
*/
private function getLayeredNetworkMock()
{
return $this->getMockForAbstractClass(LayeredNetwork::class);
}
/**
* @return ActivationFunction|MockObject
*/
private function getActivationFunctionMock()
{
return $this->getMockForAbstractClass(ActivationFunction::class);
}
}